Deep Learning Approaches for Impulse Noise Mitigation and Classification in NOMA-Based Systems

Abstract

The new emerging networks such as smart grids, smart homes and Internet of Things have enabled user accessibility across the globe and employ non-orthogonal multiple access (NOMA) scheme to accommodate huge number of connected devices. These devices which include smart meters, sensors and actuators etc. suffer from impulse noise (IN) while operating with power systems. Furthermore, NOMA scheme provides power domain multiple access (PDMA) which is found to be susceptible to IN. Based on the aforementioned IN intervention and its degrading effect on communication applications, novel mechanisms are desired to mitigate and classify the IN induced in the received signal. In this research work, novel IN mitigation and classification techniques are presented using deep learning methods for NOMA-based communication systems. The IN detection is performed by first identifying the IN occurrences using a deep neural network (DNN) which learns statistical traits of noisy samples followed by removal of harmful effect of IN in the detected occurrences. Using the proposed DNN, higher bit error rates (BER) were achieved when compared with the existing IN detection methods. The proposed method was further validated for high and low IN, and weak and strong IN occurrence probabilities. Moreover, another deep learning network is proposed in this research work to effectively distinguish between high IN and low IN in the noise contaminated NOMA symbols which can help improve the performance of IN detection models. Both of the deep learning methods proposed in this study show strong potential to address IN problem faced by the NOMA scheme.

Keywords

• Deep neural network (DNN)
• impulse noise (IN)
• multiuser communication
• non-orthogonal multiple access (NOMA)
• power division multiple access (PDMA)
• successive interference cancellation (SIC)